1. Technical Field
The present disclosure relates to pulmonary embolism detection and, more specifically, to reduction of lymph tissue false positives in pulmonary embolism detection.
2. Discussion of Related Art
A pulmonary embolism (PE) is a medical condition characterized by the partial or complete blockage of an artery within the lungs. Pulmonary emboli (PEs) can be life-threatening. For example, one in every three cases of PE generally results in death. Moreover, the occurrence of PEs has been increasing.
If accurately detected, PEs may be treated with the administration of anti-clotting medications. However, accurate diagnosis has been difficult, and is not properly identified in approximately 70% of all true PE cases.
Accordingly, accurate identification of pulmonary emboli can significantly reduce the number of missed PE identification and accordingly, lead to more timely treatment and ultimately, save lives.
Recently, approaches for detecting PEs using computed tomography (CT) medial imaging have gained popularity. Here, the patient's chest may be imaged and the resulting image data may be carefully analyzed for signs of a PE. However, due to the difficulty in distinguishing a PE from non-PE structures and image artifacts, detection of PEs using CT imaging is often prone to false positives. These identification problems may be particularly acute when looking for PEs within lymph tissue. Additionally, as the process of manually inspecting the image data can be long and tedious, limitations of human attention span and eye fatigue increase the opportunity for misidentification.
Accordingly, attention has been given to finding methods for automatic PE detection within medical image data. In automatic detection, the medical image data is analyzed by a computer system so that one or more regions of suspicion may be identified. The identified regions of suspicion may then me highlighted or otherwise brought to the attention of a medical professional, such as a radiologist, so that in reviewing the medical image data, particular attention may be given to those areas found by the computer system as having the greatest probability of being PEs. However, as discussed above, such approaches for the computer-aided detection of PEs have been particularly prone to false positives, especially in lymph/connective tissue. In fact, false positives within these areas may account for approximately one in three of all false positives.